Assessment of Primary Production by Statistical Analysis of Water‐quality Data

Abstract

Time series of weekly water-quality data at Schnackenburg on the Elbe River (1985-2000) were subjected to principal component analysis (PCA). Considering the amplitudes of composite patterns of variables is a step towards a process-oriented interpretation of water-quality data. One specific objective was to investigate the impact of improved water quality after the German reunification in 1990 on primary production and the oxygen budget. To discriminate anthropogenic signals from natural fluctuations a separation of the impact of discharge was attempted based on a linear regression approach. A dominant pattern of co-variation in the residual data could be attributed to biological activity (primary production). The most relevant variables of this 'biomode' are oxygen saturation, pH, and orthophosphate. We conclude that multivariate statistical analysis of water-quality data can help to estimate primary production when direct observations of algal concentrations are missing. In the years from 1998-2000 the trend of the 'biomode' indicates an increased load of oxygen consuming biomass caused by enhanced primary production in the middle stretches of the Elbe River which corresponds with the observation of more severe oxygen deficits in the tidal section of the river.